Abstract
Satellite soil moisture and vegetation optical depth [(VOD); related to the total vegetation water mass per unit area] are increasingly being used to study water relations in the soil-plant continuum across the globe. However, soil moisture and VOD are typically jointly estimated, where errors in the optimization approach can cause compensation between both variables and confound such studies. It is thus critical to quantify how satellite microwave measurement errors propagate into soil moisture and VOD. Such a study is especially important for VOD given limited investigations of whether VOD reflects in situ plant physiology. Furthermore, despite new approaches that constrain (or regularize) VOD dynamics to reduce soil moisture errors, there is limited study of whether regularization reduces VOD errors without obscuring true vegetation temporal dynamics. Here, we find that, across the globe, VOD is less robust to measurement error (more difficult for optimization methods to find the true solution) than soil moisture in their joint estimation. However, a moderate degree of regularization (via time-constrained VOD) reduces errors in VOD to a greater degree than soil moisture and reduces spurious soil moisture-VOD coupling. Furthermore, despite constraining VOD time dynamics, regularized VOD variations on subweekly scales are both closer to simulated true VOD time series and have global VOD post-rainfall responses with reduced error signatures compared to VOD retrievals without regularization. Ultimately, we recommend moderately regularized VOD for use in large scale studies of soil-plant water relations because it suppresses noise and spurious soil moisture-VOD coupling without removing the physical signal.
Highlights
VEGETATION OPTICAL DEPTH (VOD) and soil moisture, as remotely measured from microwave spaceborne sensors, are widely used across geophysical investigations
We argue that the dual channel algorithm (DCA) having less frequent VOD increase responses is due to measurement error artificially inducing a positive relationship between soil moisture and VOD (Figs. 6 and 9)
If the Multi-Temporal Dual Channel Algorithm (MT-DCA) regularization suppressed subweekly VOD dynamics as in a low-pass filter, the spatial pattern shown using the simultaneous retrievals (DCA) would not persist and the percentage of storms where VOD increases computed based on the regularized VOD (MT-DCA) would converge to 50% indicating no consistent response
Summary
VEGETATION OPTICAL DEPTH (VOD) and soil moisture, as remotely measured from microwave spaceborne sensors, are widely used across geophysical investigations. Soil moisture and VOD are traditionally retrieved simultaneously where both variables are estimated from horizontally and vertically polarized microwave brightness temperature measurements (TBH and TBV, respectively) at each satellite overpass Such retrieval algorithms, referred to here as simultaneous retrieval approaches, include the dual channel algorithm (DCA) and land parameter retrieval model (LPRM) [22], [30], [31]. Smoothing a retrieved time series with a low pass filter is not equivalent to the time derivative regularization discussed here This is because smoothing explicitly removes short timescale variability without the benefits of stabilizing the optimization and suppressing errors by imposing a priori information [33]. The regularization approach discussed here that constrains the VOD time derivative assumes that the vegetation temporal dynamics that VOD represents occur slower than surface soil moisture changes.
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